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article · Informatica · 2024

ChatGPT Tweets Sentiment Analysis Using Machine Learning and Data Classification

Aliea Salman Sabir, Huda Adil Ali, Maalim A. Aljabery

Cited by 12Open access

Abstract

Many things, such as goods, products, and websites are evaluated based on user's notes and comments. One popular research project is sentiment analysis, which aims to extract information from notes and comments as a natural language processing (NLP) to understand and express emotions. In this study we analyzed the sentiment of ChatGPT labeled tweet datasets sourced from the Kaggle community using five Machine Learning (ML) algorithms; decision tree, KNN, Naïve Bayes, Logistic Regression, and SVM. We applied three feature extraction techniques: positive/negative frequency, a bag of words (count vector), and TF IDF. For each classification algorithm. The results were assessed using accuracy measures. Our experiments achieved an accuracy of 96.41% with SVM classifier when using TF- IDF as a feature extraction technique.

Cite this paper

APA 7

Sabir, A. S., Ali, H. A., & Aljabery, M. A. (2024). ChatGPT Tweets Sentiment Analysis Using Machine Learning and Data Classification. Informatica, 48(7). https://doi.org/10.31449/inf.v48i7.5535

MLA 9

Sabir, Aliea Salman, et al. “ChatGPT Tweets Sentiment Analysis Using Machine Learning and Data Classification.” Informatica, vol. 48, no. 7, 2024, https://doi.org/10.31449/inf.v48i7.5535.

Chicago

Sabir, Aliea Salman, Huda Adil Ali, and Maalim A. Aljabery. 2024. “ChatGPT Tweets Sentiment Analysis Using Machine Learning and Data Classification.” Informatica 48 (7). https://doi.org/10.31449/inf.v48i7.5535.

Harvard

Sabir, A.S., Ali, H.A. and Aljabery, M.A. (2024) ‘ChatGPT Tweets Sentiment Analysis Using Machine Learning and Data Classification’, Informatica, 48(7). Available at: https://doi.org/10.31449/inf.v48i7.5535.

BibTeX
@article{sabir2024chatgpt,
  title = {{ChatGPT Tweets Sentiment Analysis Using Machine Learning and Data Classification}},
  author = {Sabir, Aliea Salman and Ali, Huda Adil and Aljabery, Maalim A.},
  journal = {Informatica},
  volume = {48},
  number = {7},
  year = {2024},
  doi = {10.31449/inf.v48i7.5535}
}

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